无人驾驶水下车辆的螺旋自动抑制噪音算法基于一个两阶段的消噪-涂漆框架
Yu Zhao1,2, Ji Xu1,2, Yuan Xie1,2
1Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences, Beijing, 100190, China.
The Journal of the Acoustical Society of America
|March 2, 2026
概括
本研究引入了一种新的两阶段框架,以减少无人驾驶水下车辆 (UUV) 的自我噪音,通过有效抑制调制干扰和重建光谱图,显著提高目标识别精度.
科学领域:
- 水下声学 水下声学
- 信号处理 信号处理
- 机器人技术 机器人技术 机器人技术
背景情况:
- 无人驾驶水下车辆 (UUV) 自动噪音严重降低了被动检测和识别.
- 现有的无色化方法无法充分解决调制干扰和光谱孔的问题.
研究的目的:
- 开发一个先进的无声化涂装框架,以克服当前UUV自我降噪技术的局限性.
- 提高目标信号的保真度,以改善水下声学检测.
主要方法:
- 一个两阶段的框架,结合了基于面具的消除噪音网络和频谱染色网络 (SINet).
- SINet集成了一个调制-孔恢复模块,并利用了一个轴频抑制损失.
- 框架应用于ShipsEar数据集和收集的UUV自噪数据.
主要成果:
- 有效地抑制调制干扰和减少光谱孔.
- 干扰轴频率的峰值与平均值比率减少了75%;光谱平均平方误差减少了22%.
- 在目标识别准确度方面实现了6.87%的改进.
结论:
- 拟议的框架显著提高了UUV被动检测能力.
- 该方法有效地恢复光谱信息,并减轻噪声引起的干扰.
- 这种方法为UUVs的水下声信号处理提供了实质性的进步.
相关概念视频
01:18Uniform Depth Channel Flow: Problem Solving
568
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
568
01:07Time-Domain Interpretation of PD Control
421
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
Consider the example of control of motor torque. Initially, a positive...
421